



'''simple docstring'''






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   str	  =	inspect.getfile(accelerate.test_utils      )
      a		:   Optional[Any]	  =	os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
      a		:   Optional[int]	  =	os.path.sep.join(
          mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
      a		:   int	  =	os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_ops.py']      )







    @require_multi_gpu
    def      lowercase_			( self					:    Optional[int]      ):
      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a		:   List[Any]	  =	['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
        execute_subprocess_async(__snake_case		,     env=os.environ.copy()      )







    @require_multi_gpu
    def      lowercase_			( self					:    Union[str, Any]      ):
      print(F"""Found {torch.cuda.device_count()} devices."""      )
      a		:   int	  =	['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(F"""Command: {cmd}"""      )
      with patch_environment(omp_num_threads=1      ):
        execute_subprocess_async(__snake_case		,     env=os.environ.copy()      )







    @require_multi_gpu
    def      lowercase_			( self					:    Tuple      ):
      a		:   Union[str, Any]	  =	['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      with patch_environment(omp_num_threads=1      ):
        execute_subprocess_async(__snake_case		,     env=os.environ.copy()      )







    @require_multi_gpu
    def      lowercase_			( self					:    str      ):
      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      a		:   str	  =	['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1		,     cuda_visible_devices='0,1'      ):
        execute_subprocess_async(__snake_case		,     env=os.environ.copy()      )


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		Accelerator()
     lowerCAmelCase:   Optional[Any]					=		(accelerator.state.process_index + 2, 1_0)
     lowerCAmelCase:   Dict					=		torch.randint(0, 1_0, shape).to(accelerator.device)

     lowerCAmelCase:   Tuple					=		''

     lowerCAmelCase:   Optional[Any]					=		accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowerCAmelCase:   int					=		accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowerCAmelCase:   Union[str, Any]					=		accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)